Profitability and financial performance of Italian real estate companies: quantitative profiles Open Access

The broad scope of the real estate market includes constructing and selling real estate and assets of high economic and social value (Yi Man Li, 2016; Serrallegri, 2022). It thus includes construction, demolition, renovation, redevelopment and maintenance of buildings and infrastructure, as well as a complex variety of advisory and intermediary services that facilitate trade. The sector's production units can therefore be classified into two macro-categories.

Construction companies, also known as building contractors, are in charge of material works (Anderton, 2022; Abdul-Kareem, 2015; Panniello et al., 2022), soliciting the supply of raw materials. They are often involved in multiple construction sites. Weather situations influence their activity. They can use smaller companies to coordinate their activities. They can produce for the market or even on commission, perhaps after winning a tender (Cianflone et al., 2018). They can realise more or less large and complex buildings. A distinction is often made between residential (Vabischevich, 2021), non-residential and public works construction. Significant money, liquidity and specialised managerial skills often characterise developers and investors in managing urban construction projects. This is why special real estate investment funds have developed, which are in addition to the other financial operations typical of institutional investors and real estate management and leasing companies (Flynn, 2018). The sector also significantly impacts the household economy, considering the high costs of purchases and rents, especially in the most desirable areas, which can generate substantial profits for owners (Leonard, 2022).

Real estate companies facilitate the matching of supply and demand for real estate (Gardner, 2011; Castello, 2003; Waterson and Lee, 2001a, 2001b), also using real estate and financial advisors. The broad category thus includes real estate buying and selling activities carried out on one's property, rental and management of owned or leased real estate, and real estate activities for third parties (real estate agencies, which are widespread). Sometimes in franchising (Galli and Stella, 2021) or even “chain” (Bishop, 1993), they play a key role in making the real estate market efficient (Kunzel, 2009), even where a real estate exchange is active, providing up-to-date information and protecting the parties, reducing time and risk for the contracting parties.

In summary, they provide the following:

1. technical, contractual and financial advice with customised services;

2. facilitate access to credit, as they are often linked to specialised financial institutions;

3. they facilitate collateral services (insurance, restructuring, etc.);

4. they reduce the time of transaction conclusion (Bronchick and Cooper, 2016);

5. they lower the risks in sale and purchase transactions due to their specialised expertise.

The real estate sector, as a whole, plays a central role in a nation's economic development, including in Italy, where it contributes to employment, production and the country's financial stability (Rossi, 2019; Nomisma, 2020; Scenari-immobiliari.it, 2021). According to a recent edition of the report on the real estate services sector in Europe and Italy (Lunghini, 2021), the sector in European countries produces an added value of about 20% of the total: in 2020, the turnover of the real estate services sector in the five main European countries was estimated at 350bn euro, with a 6.3% decrease compared to 2019 due to the lockdown.

While the Italian and international bibliography has devoted ample space to construction and related financial services, studies on the performance of Italian real estate companies are rarer, to which this study is dedicated. It presents the economic-financial dynamics of a large sample of companies over the decade 2011–2020 and outlines possible post-pandemic recovery strategies.

Using exclusively financial statements data and indices derived from their analysis appropriately processed with suitable statistical algorithms, we present a study conducted in the nation and its three macro-regions (North, Centre and South) to answer a research question:

Italy can be a valuable reference for other states with similar socio-economic and urban contexts.

A historical analysis, up to recent times, of the results achieved by companies in the sector encourages its development, also by proposing management strategies based on data processing that is also useful for improving forecasts of future market trends. Therefore, it introduces a more intimate link between accounting systems and decision-making processes. Given the importance of the sector in Italy and the main advanced economies, the expected benefits are easy to see, especially regarding employment.

After the primary bibliographical references, the methodology, results, implications, limitations of the research and possible future developments are outlined.

The real estate market influences a nation's economic activity through various channels. If real estate prices rise, the expected return on the construction business increases, and as a direct consequence, capital accumulation in the sector also increases (Rossi, 2019). The economy benefits because construction activates the supply chain, amplifying cyclical stimuli: an owner household, sensing the increase in wealth, tends to spend more. The value of the collateral often required for bank loans also increases, with effects on the cost and quality of banks' assets and, consequently, on the availability of credit (Shafik, 2006), with intuitable influences on monetary policy and the economic cycle (Giannotti and Gibilaro, 2009).

The subprime mortgage crisis in the United States of America at the origin of the global crisis of 2008–09 was an example of an increase in impaired loans, with a consequent impact on the ability to make new loans, which had a considerable impact on households and businesses (Lopreite and Scarpino, 2010).

There are also effects on taxation, considering, for example, the deductibility of interest expenditure on mortgages and the frequent tax breaks for purchasing a first home (Milligan et al., 2009; Ong et al., 2013; Barrett et al., 2015).

Facilitating or incentivising home purchases may reduce residential mobility instead of helping facilitate development. Some studies confirm the inverse correlation between home ownership and mobility (Troiano, 2022). However, high transaction costs in buying and selling houses and very stringent regulations of the rental market can hinder mobility.

A clear sign of the efficiency of the real estate market is the high flexibility of access to housing services, whether owned or rented: if, on the one hand, it contributes to the territorial redistribution of workers to job opportunities, on the other hand, it panders to the desire for independence of younger people, both when they are out of the country for study reasons and when they decide to start a family (Arbaci et al., 2021). The labour market thus benefits, along with the growth potential of the economy and collective well-being.

Investments in real estate are largely financed through debt, and leverage is generally high (Herring and Wachter, 2002). If financing on favourable terms is available, the investment would be higher. Therefore, a significant share of bank income, rather than risk, depends on real estate.

The most significant and widespread risks are price bubbles, which “burst” and reduce the ability of borrowers to repay loans, collateral is devalued, and as a result, lenders suffer considerable losses. This happened in Northern European countries in the early 1990s, in Southeast Asian countries in 1997 and in Japan at various stages in the 1990s, undermining the soundness of financial intermediaries (Rossi, 2019).

Therefore, the real estate sector is experiencing rapid and marked changes stemming from new technologies and their impact on the different stages of the real estate life cycle (Janowski and Lepkova, 2021).

The challenge in the future lies above all in financial dynamics, as well as in production techniques, requiring more outstanding qualifications consultants and appraisers (Tsung, 2022) who will have to develop new skills capable of monitoring the entire technical and financial production process (Baiardi and Tronconi, 2010).

The real estate company performs services related to real estate (Cermignano and Fasano, 2011). This initial and simple definition excludes construction companies, which instead deal with physical-technical interventions, as already mentioned.

Real estate companies, therefore, carry out the following activities related to real estate:

1. buying and selling their property or that of third parties;

2. management, leasing or letting;

3. mixed activities, combining the above;

4. intermediation between supply and demand, typical of classic real estate agencies, compensated by a commission on the purchase price, which mainly remunerates the information, skills and protection offered, reducing the risks of contractors to whom personalised services such as technical, contractual and financial advice are offered;

5. leasing, with the possibility of redeeming the asset used (Sirtoli, 2013).

6. financial, management and valuation consultancy on real estate with appropriate valuation methodologies.

The Ateco 68 code, which is the subject of this research, uses this classification in Italy. It excludes construction companies to which the Ateco F code: Construction is instead dedicated.

To be competitive in the global market, companies must have available capital and adequate managerial skills, and to face the growing competition from real estate investment funds and other financial operators such as life insurance companies, provident societies and pension funds.

The scholarly focus on the sector, in general, is significant.

Several analyses, including some recent ones, refer to construction companies. For example, we refer to the writings of Haider et al. (2023) and Khalfan et al. (2022) on leadership and the effects of managerial culture, the research on sustainability by Bontempi et al. (2023), the publication of new communication strategies (Schnell, 2022), the analyses on the use of risk factor models (Escribano et al., in press), etc.

There are also numerous publications on the real estate market and investment possibilities (Jayanthi and Saravanakumar, 2022) in a sector that could be very profitable. In this context, we note the study by Tang et al. (2022), who analysed stock market responses and the movement of returns. In China, Huang et al. (2022) assessed the systemic financial risks arising from the pandemic.

The analyses of regulated financial markets are complemented by those of classical bank financing methods (Abuamsha, 2022).

Specific studies have recently been devoted to changes in prices (De Stefani, 2021; Tuyet et al., 2022) and brokerage costs, which have a significant impact on the dynamism of trading (Lee and Lee, 2007; You et al., 2014).

We then highlight the evolution of strategies (Rowley, 2005) due to changes in size and asymmetric information typical of the industry (Bishop and Megicks, 2002a, b).

More recently, the adoption of information and communication technologies (Li and Wang, 2006; Babatunde and Ajayi, 2018) and the use of “virtual reality” (Adegoke et al., 2022) have changed long-term choices. New technologies have also changed customer relations, evolving traditional marketing (Driver, 1984; Pheng and Hoe, 1994) to digital marketing (Luís et al., 2022), which has undoubtedly altered the more classical modes of relationship between supply and demand. This is also due to the observation that home buyers and tenants increasingly rely on the Internet to search for accommodations (Koch and Maier, 2015) and the frequent integration of big data into the real estate decision-making process (Cheryshenko and Pomernyuk, 2021).

Despite these developments due to new technologies, a special relationship with the customer due to the specificity of the intermediated property and it is often high-cost characterises real estate companies: customer identification, participation, satisfaction and loyalty are always relevant to the success of the economic initiatives (Yang et al., 2019), which must carefully consider the decision-making process, even if irrational, in the purchase of agency services, and the key role played by the staff in “shaping” consumer behaviour: price and the physical appearance of the service provider are certainly not among the main evaluation criteria (Crozier and McLean, 1997).

More recent developments give a prominent place to sustainability requirements for a sector that certainly needs specific knowledge (Filstad and Gottschalk, 2009), information (Grishkina et al., 2022), and training to generate value always and, above all, to develop the processes of the circular economy (Gupta and Tiwari, 2022).

In this context, the quality of life of employees (Majumder and Biswas, 2022), who suffer from the described effects of the digitisation of their activities (Piazolo and Dogan, 2021), is important, even considering that they are often forced to note that ethnic discrimination is a persistent problem, predominantly in the private rental market: discrimination is lower in poorer, ethnically mixed neighbourhoods and higher in smaller, male-dominated real estate offices (Ghekiere and Verhaeghe, 2022).

Real estate companies, however, also sometimes behave unethically (Markoc and Cizmeci, 2021). Therefore, prevention, monitoring of activities and possible sanctions are necessary on the part of public authorities, which should propose a clear legislative framework (Curran and Schrag, 2000). The state must also intervene to support the activities of these companies, especially to improve the effectiveness of agency disclosure in real estate transactions (Wiley and Zumpano, 2009).

One of the main economic effects of the pandemic has been the spread of remote working. This has favoured living away from the workplace, thus decongesting the main urban centres and favouring suburban living. This, of course, has changed the value of real estate, which, in some cases, has relatively decreased in large cities and, conversely, increased in suburbs and smaller towns. This was an actual market revolution with effects that have been well analysed by Gupta et al. (2022). A similar study approach, with similar outcomes, characterised the research by Chmielewska et al. (2022), which shows a trend of increasing demand for residential properties away from cities in favour of urban suburbs and areas close to green spaces. Such situations can also be observed in Italy (Micelli and Righetto, 2022).

Trends in the real estate market were only marginally affected by public interventions (Pilinkiene et al., 2021). Panic has often led to irrational behaviour in purchasing durable goods such as real estate and cars (Karthiyaini et al., 2020).

In general, however, it seems that the processes of migration of values to and from the sector do not show any relevant changes when comparing the periods before and after the pandemic, at least in Poland (Kowalski et al., 2023). A more comprehensive analysis, however, must also consider the choices of tenants and not only those of investors, which are more widely studied (Munshifwa et al., 2021).

The nation that originated the pandemic and in which the most remarkable effects were felt was China. Therefore, the aforementioned study by Huang et al. (2022), in which the systemic financial risks in the real estate market resulting from the pandemic were assessed, is particularly interesting. The Chinese situation was also compared with that of the United States and Europe (Jovanović-Milenković et al., 2020), concluding that, even though the viral pandemic appeared at different times around the world and with different intensity, the indicators of changes in the real estate market point to similar trends.

Also interesting because of the spread of COVID-19 are the proposals of Min et al. (2022), who quantified the daily rate of return of three major stock indices of real estate-related industries.

The scholars' efforts have also analysed the volatility of “green metal” prices and its links to environmentally sustainable real estate development over the pandemic (Eje et al., 2023), considering the described sustainability drivers.

It should be mentioned, in conclusion, that COVID-19 had a considerable impact on the workflow of real estate brokers and the attitudes of their clients (Marona and Tomal, 2020).

Numerous contributions, therefore, on the relationship between the pandemic and the real estate market, but little is based on financial statements data, which justifies one of the research hypotheses of this paper later outlined.

Concerning Italy, first of all, there are studies on the economic impact of monetary policy (Ahmed et al., 2021), unconventional (Ahmed et al., 2020), and analyses on equity crowdfunding (Battisti et al., 2020), as well as monographic writings in local languages (Baiardi and Tronconi, 2010; Cermignano and Fasano, 2011).

Italian real estate has also been the subject of specialised analyses of closed-end real estate funds as part of a broader analysis of real estate portfolio management (Mattarocci and Scimone, 2021), as well as applications of a subset of artificial intelligence, “machine learning”, to detect market information transparency (Gabrielli et al., 2022) and to predict house prices (Rampini and Re Cecconi, 2022).

Italy has been plagued by the pandemic, and therefore its experience is significant for assessing the consequences of COVID-19 on real estate supply and demand. The study mentioned above by Micelli and Righetto (2022) compared the responsiveness of demand after the financial crisis of 2008 and the pandemic crisis of 2022 in major metropolitan cities. The authors distinguished different clusters in which they sometimes recorded a significant increase in property values and other times a decrease in favour of more sustainable housing areas.

The Italian market, therefore, which was also investigated with the analyses of Avallone and Quagli (2015), can be a valuable laboratory for studies based on currently deficient company financial statements data, especially concerning the effects of the pandemic.

This topic has been the subject of numerous contributions addressing it from different perspectives.

The organisational dimension is prevalent in the recent contribution by Amos and Boakye-Agyeman (in press), who attempted to model the value added of corporate real estate precisely on organisational performance. The authors constructed statistical relationships between indicators of the value added by corporate real estate and the financial and non-financial performance of the organisation. They then highlighted the significant influence of three value-added indicators on organisational performance, highlighting the need for strategic management of corporate real estate risk to improve performance.

Gender, age and tenure of executives, however, are not a catalyst for differences in corporate performance. Ricciotti et al. (2022) came to this conclusion by drawing on the Upper Echelons theory and analysing the socio-demographic characteristics of key executives of a panel of listed European real estate service companies. Instead, financial statements outcomes are more directly related to intellectual capital (Zhang and Wang, 2010) and salesperson motivation, especially if transactions are online (Gautier et al., 2023). The prior participation of employees in the setting of budget targets would also seem to be very helpful in improving their performance (Silva et al., 2023).

Other contributions focus on corporate performance by correlating it with developments in the financial markets. The topic is well outlined in Ke et al. (2008), where they show that, in England and Wales, it is subject to the volatility and uncertainties of the market, its “liquidity” and thus to changes in house prices. Business factors such as size and fees are much less affected.

Finally, in other research, the outcomes of financial statements and the calculation of key ratios, which are also used in this article, take precedence.

Already in 2018, Rehkugler et al. examined whether and to what extent open real estate companies could use financial statements information to identify undervalued companies. More recently, in 2022, Khuntaweetep and Koowattanatianchai created an econometric model for strategic planning and ROE forecasting of real estate development companies in Thailand. Even more recently, in 2023, Gupta et al. examined the impact of mergers and acquisitions on the financial performance of the construction and real estate industry using numerous ratios. Coeval is the contribution of Jiang and Zhang (2023), who also used financial ratios to analyse the relationship between the debt ratio and the performance of numerous real estate companies, also distinguishing state-owned from private.

The use of the ANOVA method to develop ratios, also used in this article, characterised the research of Daryanto et al. (2018) to assess the impact of liquidity and leverage on the performance of Indonesian firms. Finally, it is worth mentioning one of the few Italian cases due to Avallone and Quagli (2015), who used profitability drivers and financial structure for industry studies.

The conceptual framework and the articulate presentation of the main bibliography revealed a considerable interest of the scientific community in the field, also considering its economic importance in all nations. Scholars also highlighted the importance of financial statements and their informative power to gather news for their research objectives. There are also relevant studies on the performance of companies that have used static and dynamic analyses of financial statements and statistical processing methods such as, for example, ANOVA. Italy has also been the subject of significant elaborations.

Although there has been a proliferation of studies on the real estate sector in recent months, especially to assess the effects of the pandemic, there is a lack of quantitative analyses using financial statements data of Italian companies in the decade 2011–2020. This prompted this research to implement the literature on real estate agencies and test the following hypotheses:

Health and economic crises characterised the two years 2020–2021, which, however, did not radically affect the real estate services industry, which showed strong resilience. The construction and real estate sectors, within this general framework, continued to play a central role in the economic systems of the various countries, of which they contribute 17.4% to the formation of added value (up by one percentage point compared to the year before the pandemic) (Lunghini, 2021).

According to the seventh edition of the report on the real estate services sector in Europe and Italy produced by Scenari Immobiliari (Scenari-immobiliari.it, 2021), the sector under analysis, with the construction sector continues to play a crucial role in the economies of the main European countries, contributing an added value representing between 18 and 21% of the total (Table 1).

Real estate services in the core European countries recorded an estimated turnover of around EUR 350bn in 2020, down by about 6% compared to the previous year. This is the somewhat predictable result related to the pandemic, where there were periods of complete standstill of activity. The only European country with a better-performing market was Germany, recording a turnover of almost EUR 125bn and a moderate decline of 1.7%. France, on the other hand, kept its business volume unchanged, so 2020 ended with a turnover of EUR 85.2bn.

Italy recorded a turnover of almost EUR 37.1bn, with a drop of almost twelve percentage points compared to 2019.

The country, on the other hand, that recorded the most negative figure ever, with a loss in turnover of almost twenty percentage points, going from 91bn in 2019 to 73.3bn, was the United Kingdom, for which 2020, in addition to being the year of the pandemic, was the year of its exit from the European Union. By contrast, the only country that did not record a negative trend was Spain, thanks to brisk activity, especially in the latter part of 2020. To this result, Spain is the only country with a growing turnover (+1.4%).

On the employment front, the construction sector is of key importance, recording remarkable figures. Also relevant is the presence of real estate services companies, the subject of this analysis (Table 2).

In Italy, the percentage of the total workforce respects the European average, while the average company size is lower, i.e. 1.9 employees per company. The United Kingdom and Germany are exceptions, with an average size of 7.4 and 4.7 direct employees, respectively, while a significant fragmentation of companies characterises France, Spain and Italy (Lunghini, 2021) (Table 3).

The real estate sector is central to the Italian economy because of its contribution to employment, production and the country's financial stability. Despite the small size of the companies and the vast fragmentation of the production structure as well as under capitalisation, difficulties in accessing financing and the complex management of receivables from the public administration (Federcostruzioni.it, 2012).

In Italy, almost one-fifth of the GDP in one year corresponds to the total value of investments and expenditure on rents and brokerage services (Rossi, 2019). Sixty per cent of total household wealth is invested in real estate assets. Loans to households for real estate mortgages and loans to businesses in this sector account for about one-third of total bank loans.

Italy's construction tradition is significant, but optimal organisational and management activities are less widespread, even concerning all services for the real estate sector.

The reasons behind this situation are (Caracciolo, 1963; Gnan and Montemerlo, 2008).

1. the historical delay in the industrialisation process, which is responsible for the deficit of organisational and management culture;

2. an entrepreneurial fabric made up of small and minute enterprises with solid craftsmanship.

The situation is slowly improving. This is mainly due to the process of financialisation of the Italian real estate market, which has had as a direct consequence the creation of new financial instruments (Baiardi and Tronconi, 2010).

However, the market has been surprised by its resilience to the problems of the pandemic crisis (Buonaccorsi, 2021). 2020 had a more modest impact than expected. Experts consider Italy to be ready for recovery that, albeit slightly, may even rise above the levels estimated for the German economy, which has always been a benchmark for European countries (Nomisma, 2020).

There are, however, two elements to be considered, namely, the 2% drop in employment in 2020 and lower disposable income for households (International Labour Organisation, 2020). Despite this, the forecasts are optimistic due to a typical feature of the Italian real estate market: as many as 8 of 10 households have recourse to credit for the purchase of a home (Marcatili, 2021) due to a lack of liquidity because in any case, the home is still a haven asset, due to the numerous concessions for the purchase of a first home, etc. (Nomisma, 2020).

Indeed, the 2020 lockdown has, however, substantially changed the modus operandi of agents who have reacted by digitising agencies and by using video appointment services, virtual tours, online cadastral maps, etc. (Bergamaschi, 2022).

The forecasts are optimistic, not least because of the expected benefits of the PNRR (Bergamaschi, 2022) and the increase in demand for properties located in small towns (Immobiliare.it, 2022) to buy larger premises.

Considering demographic trends and greater sensitivity to the logic of sustainability, efforts are also being made to recover the already built-up area without consuming new land. Efforts to improve energy efficiency, which reduces energy costs and promotes a healthier environment, are also on the increase.

More and more foreigners, both European and non-European, have decided to invest in the Italian residential property market, according to a recent survey conducted by Fimaa and REinsight-Opisas (Simplybiz.eu, 2021).

In Italy, a significant boost to the market is also due to the benefits of the 110% Superbonus, which supports energy efficiency interventions in residential buildings with public funds (Mecca et al., 2020; Artese et al., 2021; Rebaudengo et al., 2022).

The investigated sample consists of 1,559 companies. It is derived from a selection of over 20,000 initial ones, removing those with unavailable or insignificant data. Thus, for all selected companies, initial data were available for all years, which were subsequently processed.

Financial statements for the decade 2011–2020, taken from Bureau van Dijk's AIDA database, were analysed. The companies were divided into the three Italian macro-regions: North, Centre and South (Figure 1).

The companies are, therefore, mainly concentrated in Northern Italy (as many as 1,040 out of 1,559), certainly the wealthiest area in the country.

The ATECO code 68 “Real Estate Activities” (Table 4) characterise all the company: companies that are therefore different but are united by the intention to produce various services in the articulated real estate market.

The sample size was assessed to turnover (Table 5) and the number of employees in 2020, the last of the survey.

Most companies have a turnover between EUR 500,000 and EUR 1,500,000. About one-third has a turnover of more than 2mn.

The size of number of employees is illustrated in Table 6.

The national average is thus 12 employees, considering the employment of 18,764 people in the 1,559 companies selected.

After obtaining the financial statements for the decade considered, the average annual value and ten-year trend of two-income ratios (ROE and ROI) and three capital structure ratios (Financial Independence Ratio, Current Ratio and Fixed Asset Coverage Ratio) were assessed.

The available data were also subjected to the following descriptive statistics to broaden the possibilities of evaluating the phenomenon: arithmetic mean, standard error, median, fashion, standard deviation, sample variance, kurtosis, skewness, interval, minimum, maximum, sum and number of observations.

In particular, it was checked whether the values were close to the arithmetic mean, in which case the trends are stable and have a better descriptive value.

After representing the historical trend with a broken straight line, the interpolating function that best interprets the trend over the period was also determined. Several functions could be used: exponential, linear, logarithmic, polynomial, power, moving average, etc.

The choice fell on the one that maximises the coefficient of determination (R²), with values close to 1: always the polynomial function of order 6.

The analysis was relative to the national average and its three macro-regions.

To ascertain the presence of statistically significant differences between the three areas, the one-way ANOVA test was used (Quirk, 2012; Liao and Li, 2018), using geographic locations as the independent variables, while the dependent is the value of the ratio. Indeed, the use of the ANOVA test has been effective in numerous studies (Kottala and Herbert, 2019; Fotopoulos and Psomas, 2009), etc.

The post-Anova test, known as the Tukey–Kramer test (Tukey, 1949, 1953, 1993; Kramer, 1956; Benjamini and Braun, 2002), has been used to identify which area differs from the others.

ROE indicates the percentage return on equity and is calculated by comparing net profit and equity. Its value should be as high as possible, far from the rate of return on government bonds often used as a benchmark.

Table 7 shows the main statistical calculations.

A very often positive ROE characterised the sector, albeit relatively low and with uncertain and fluctuating trends.

Table 8 shows the interpolation function equations for each group.

The R² value in the North and South is close to 1: the trend depicted in Figure 2 is therefore statistically valid. In the Centre, the R² value is slightly lower, so the trend deviates, albeit slightly, from optimal values.

The reverberation of the 2008 crisis is evident in the two years 2012–2014 after the “rebound” of the years following the global financial crisis, which in Italy had particularly severe characteristics (Istat, 2012; Borghi, 2013).

In particular, the South starts with very high values compared to the other geographical macro-areas and then records a more consistent drop in 2014 to a value of 1%. This is followed by a sharp recovery that brings the values of the South back above the other areas considered, up to values close to 4. The North and the Centre, on the other hand, show a less fluctuating trend. The North is the only one to record a decline in ROE in the last year of the decade under review.

The data were subjected to the ANOVA test to better measure and evaluate the differences between the areas (Table 9).

The variance shows an F-value of 0.84, which is lower than F (3.35): No statistically significant differences exist between the areas examined. Therefore, there would be no need for the post-Anova test, which occurs later on, for appropriate verification.

ROI [Operating income/(Shareholders' equity + Bonds + Long-term liabilities to banks + Long-term liabilities to shareholders + Long-term liabilities to shareholders] is a summary percentage indicator of the effectiveness and efficiency of operations. It is often compared with the cost of capital: ROI should be higher than the prevailing interest in financial markets to have a positive effect from “leverage” (Tieghi and Gigli, 2009). Descriptive statistics are given in Table 10.

Also, for the ROI, the interpolating function used is the polynomial of degree 6. The R² coefficient varies from 0.79 to 0.96, so the constructed polynomial proxies are statistically quite significant (Table 11).

A graphical representation of the trend is shown in Figure 3.

The sector has a good operating return on invested capital everywhere. The North, the Centre and the South have equally satisfactory values with similar development trends. The presence of any statistically significant differences between the three macro-areas is then verified with the ANOVA test (Table 12).

Here again, the null hypothesis H0 is established, and thus the absence of statistically significant differences between the three geographical areas under consideration, as the F-value of 1.19 is lower than the critical F-value of 3.35. There would therefore be no need for the post-Anova test, which, in any case, occurs later on for appropriate verification.

The financial independence ratio shows the relationship between venture capital and investments and, thus, the percentage the company depends on or is independent of debt capital. Exactly the formula used is (Equity/Total Assets) * 100. The results of this study are given in Table 13.

Table 14 shows the equations needed to obtain the interpolating trend curves and, with R², their representative effectiveness.

The equations show that the R² value for the Centre is close to 1: the trend is statistically significant. For the North and South, however, it only approximates the broken line well. This trend is graphically expressed in Figure 4.

The index of financial independence shows an increasing trend for the Centre, while for the South and North, it is unstable. The measurement and evaluation of the difference between the groups are entrusted to the ANOVA test (Table 15).

The analysis of variance shows that the F-value (13.14) is higher than the critical F-value (3.35). The post-Anova test, later presented, is required.

The current ratio assesses a company's working capital by measuring the ratio of its current assets to its current liabilities. It shows the company's ability to meet its short-term liabilities with its short-term liquid assets, which consist of inventory, receivables due within 12 months, and cash.

A desirable ratio greater than 1 indicates that short-term assets are greater than readily collectable liabilities. The results of the quantitative analysis are given in Table 16.