A Data Analysis of the Relationship Between Life Quality Indicators and the Real Estate Market in Italian Provincial Capitals

Abstract

With regard to the Italian context, the present research aims to empirically assess whether and to what extent real estate market dynamics (prices and vibrancy levels) are influenced by the life quality in a specific reference area. In particular, the study compares parameters related to the residential real estate market—such as the Real Estate Market Observatory quotations and the real estate market intensity index (used as a proxy for market dynamism)—with the Life Quality index developed by the study center of the Italian newspaper “Il Sole 24 Ore” for the selected provincial capitals. Furthermore, by breaking down the Life Quality index into the individual indicators used for its elaboration, the research identifies those most closely linked to real estate market mechanisms to explore these relationships within each context. This approach allows for the identification of potential local differences, providing insights into the degree of geographical heterogeneity. Finally, a GIS-based analysis is employed to graphically represent the various indicators, capturing the potential spatial correlations related to phenomena where the geographic component plays a significant role.

1. Introduction

The notion of life quality is increasingly the focus of theoretical and empirical research in the urban economics field, mainly because it is evident that community well-being influences urban growth and, consequently, real estate market dynamics.

The level of the quality of an urban space (understood as areas that are part of an urban context, including both public and private infrastructures and buildings) in which properties are located is, in fact, an element that is unavoidably associated with the quality of human life, and it can be interpreted as its fundamental component or as a closely related factor. In fact, a clean, safe urban area, efficiently connected to the different parts of the city, equipped with adequate public areas and that is esthetically attractive, certainly impacts the overall satisfaction of the subjects who live there or who frequent it, in terms of health and psychological well-being.

The direct relationship established between the level of the quality of the urban environment and the social, physical, mental, and economic well-being of those who inhabit it [1] demonstrates the need to develop targeted policies for city management. In this sense, the planning and design of interventions on a territory should define and coordinate the strategic actions aimed at achieving a higher level of urban quality that contributes to determining a higher quality of life for citizens.

However, it should be highlighted that an external space does not affect the life of all those who interact with it in the same way—a sense of well-being in a specific place also depends on how each person perceives that space; i.e., on a set of physiological factors capable of generating satisfaction or dissatisfaction. This mechanism is mostly dealt with by the psychological disciplines. On the other hand, economists mainly focus on the results of the physio-psychological process in order to investigate the impact of this perception on the characteristics of different urban areas and on the practical behavior of everyone [2].

In fact, the investigation of the life quality topic in the evaluative and economic disciplines has been increasing for two reasons—the first one is related to the effects of such studies in political terms since the entities of the territorial government are constantly engaged with choices on environmental, social and economic issues, directly linked to the perception of collective well-being at different scales (national, regional, and urban) [3]. Furthermore, in recent decades, a growing need to evaluate in quantitative terms the level of life quality has been attested in order to compare the detected values and to identify the most “backward” territories [4,5] which attract development policies with greater urgency. The second reason is connected to the awareness that life quality significantly affects the choices made by inhabitants and entrepreneurs regarding the localization of their home, for the former, and the implementation of their work activities and investments, for the latter. For example, Rogerson [6] analyses the explicit link between life quality considerations and the localization choices of firms and individuals, justifying the use of life quality level as a tool for the promotion of a certain place capable of attracting the attention of potential investors. Similarly, Hall [7] identifies life quality factors as central in urban development models for (i) the identification of the determinants and (ii) the subsequent proposal of mechanisms that could contribute to its improvement.

In the field of research on well-being, the two dimensions of life quality—objective and subjective—are assumed to be distinct identities [8]. The objective dimension refers to overt behaviors and conditions affecting observable factors, measured on the basis of the frequency with which the phenomenon is expressed, often aggregated and grouped into composite indices of support for implementing comparisons at the national and international level. For example, useful objective parameters are the economic wealth of a country measured in terms of gross domestic product, the average life expectancy of the population, the state of health of the inhabitants, the crime rates, the percentage of green areas, the traffic ranking, the efficiency of transport systems, the distribution of amenities and services [9,10], the human rights level, and the treatment of women, the education system (in terms of the number of schools and universities), the illiteracy and unemployment levels, etc. Objective indicators may not, however, accurately reflect the perception of well-being of the users, as this is more complex to define and, consequently, to quantify [11]. The subjective component of life quality concerns the psychological sphere of the subjects. This component aims at the individual evaluation of the conditions of life quality in terms of satisfaction with one’s own condition, the liveability of the living and workplaces, economic well-being, satisfaction with one’s main personal needs, gratification at an individual and collective level, etc. Although establishing a close relationship, the two (objective and subjective) aspects are not directly correlated and, therefore, an increase in one does not spontaneously equate to an increase in the other.

The broader scientific literature in the field of psychology addresses the different questions that arise when an individual is asked to express a global assessment of well-being level. In the process of the assessment and the formulation of a synthetic judgment, each subject refers to both the objective and subjective dimensions of well-being and, in both categories, the quality of the urban environment in which they live is included. It is evident that an adequate and/or high level of collective well-being is the result that synthesizes a complex of spatial, functional, cultural, social, and economic factors. From the single residential unit to the neighborhood, passing through the city and considering a broader perspective at a regional, national, and international level, each subject in fact measures the life quality level also through the appreciation of the space that surrounds them. This can be considered to be the domestic environment, in more stringent terms, and an urban system, in a wider view. Moreover, life quality in cities also depends on the way in which the elements that are capable of providing pleasant sensations to communities are distributed in a territorial context. Several authors have analyzed the effects of proximity to specific elements on the life quality of the city inhabitants. Among these, for example, D’Acci [12,13] has proposed a distinction between the “pleasantness deriving from urban centrality” and the “pleasantness given by the daily urban quality of the context” (named background ‘daily urban quality’ pleasantness), where urban centrality is understood as an area characterized by the presence of public buildings for cultural purposes (libraries, museums, art galleries, etc.), and/or commercial, and/or historical buildings, or single assets that constitute an attraction at a general urban level (e.g., Regent’s Park in London, Parco del Valentino in Turin, Central Park in New York, etc.). The background ‘daily urban quality’ pleasantness, instead, derives from the “beauties” of the city experienced daily by each inhabitant (the house, the office, the gardens, the squares, and the streets). With reference to the effects that urban centralities have on life quality, the author defines the Punctual Benefits, i.e., those connected to the direct use of the attraction, and the Distributed Benefits, i.e., those related to benefits given by the specific attraction to every point of the city and associated with the ease through which citizens can directly use that urban service. Analysis of the distribution of the amenities and the disamenities in a territory makes it possible to identify the urban areas for which strategic transformation interventions are most necessary and urgent in order to eliminate strong non-uniformities in the levels of quality existing between the central and peripheral contexts and to offer a “pleasant city” to all its inhabitants.

In the outlined framework, the real estate sector plays a fundamental role in determining the life quality of communities, being a proxy variable of a set of aspects considered in the evaluation processes by individuals.

In the real estate economy, the growing interconnection among the levels of urban development (infrastructures, services, building assets, etc.), the perceived level of the well-being of the subjects, and the dynamics of the property market (in terms of market supply and demand, selling prices, and rental fees) is increasingly attested.

With reference to buildings, the issue of quality—in terms of energy efficiency, safety, and comfort—directly affects the well-being of citizens. Houses equipped with modern systems that guarantee greater living comfort, in addition to reducing management costs and, therefore, influencing less the spending capacity of the subjects in relation to their specific personal income, allow for the time spent at home to be more pleasant. In this sense, the residential real estate market, particularly following the COVID-19 pandemic, is currently strongly oriented towards housing solutions with green spaces, indoor spaces that can be reconfigured according to needs, with high energy efficiency, correspondingly low environmental impact, and located in areas with adequate urban quality levels. In fact, this is one of the key points to take into consideration in real estate investment decisions, as it represents a fundamental factor in the choice processes of buyers and sellers. Thus, planning at different scales (building and territorial) aims to increase the quality of life of the users through appropriate interventions based on a holistic approach that integrates the different aspects of sustainable urban development (the social, economic, environmental, cultural, etc.).

2. Aims

The present research is part of the illustrated framework. The main focus of the analysis concerns the topic of individual and collective well-being in terms not only of economic wealth but also the level of life quality. This aspect is influenced by a wide range of the specific characteristics of the urban context (the social, economic, environmental, urban planning, landscape, cultural, etc.). At the same time, the real estate market incorporates the peculiarities of the localization of the properties in the urban context, from an infrastructural and environmental or architectural point of view. In this sense, the real estate value can be assumed to be an indirect indicator of the capacity of the physical space to contribute to the individual and collective well-being of the subjects who “live” it. With reference to the Italian context, this research aims to empirically verify how real estate market mechanisms are influenced by the level of the life quality of the specific reference context. In particular, the study pursues an investigation of whether and to what extent real estate prices take into account the extrinsic characteristics of the urban context and how much the sales vibrancy reflects the detected level of quality of life.

In particular, in the study, a comparison of the parameters related to the residential market segment (Real Estate Market Observatory—OMI—quotations [14] and real estate market intensity—the IMI indicator—used as a proxy for market dynamism) and the Life Quality (QoL) index developed by the study center of the Italian newspaper “Il Sole 24 ore” [15] for the selected provincial capitals is carried out to verify the coherence between the variables compared.

Furthermore, starting from the disaggregation of the QoL index into the different indicators that compose it, the analysis identifies those most closely related to real estate market dynamics in order to examine the relationships in each context. In this sense, any differences at the national level could be highlighted, thus deducing the connected level of geographical heterogeneity.

The contribution provided by this study concerns the identification of the main aspects that, on the basis of the results obtained, are mainly related to the processes of the formation of selling prices and market dynamism and, therefore, align with the appreciation of various Italian cities by potential investors. Through a GIS-based analysis, the different indicators can be graphed to capture any spatial correlations related to phenomena in which the positional component plays a significant role.

The present paper is organized into five sections. Section 3 illustrates an analysis of the national and international literature on the topic by highlighting the outcomes obtained with reference to different geographical contexts and time periods in terms of the influence of the life quality of communities on the real estate market. In Section 4, the analysis is presented—the comparison between the real estate parameters (the Real Estate Market Observatory quotations and real estate market intensity index) and the considered indices and indicators is carried out and the results are outlined. Finally, in Section 5, the main findings of the research are discussed and the practical implications and possible future developments are explained.

3. Literature Review

Academic research has often focused on studying the relationship between the quality of life in urban centers and the real estate market. On the one hand, some scholars have identified property prices and housing accessibility as key factors contributing to the determination of urban life quality. On the other hand, another part of the existing literature has focused on the influence of different extrinsic variables of quality of life on the real estate market. These variables are, generally, diverse in nature and encompass socioeconomic, environmental, urban, and cultural aspects.

Starting from indicators that represent physical, extrinsic characteristics influencing the quality of the urban context—in terms of service and infrastructure availability—academics have developed various indices over time to make this objective parameter measurable in order to analyze its relationship with the real estate market.

Carbonara, Faustoferri, and Stefano [16] investigated the relationship between urban quality and real estate values through a study of property values aimed at defining all the physical (and thus objectively measurable) characteristics that contribute to defining urban quality. To this end, a multiple linear regression model was developed to analyze the residential real estate market in the city of Pescara (Italy). The analyzed characteristics, in addition to considering the specificities of the asset, also include extrinsic data represented by the Urban Quality Index. The developed index was produced through the analytical and simultaneous reading of four macro-systems with the greatest impact on urban quality—the environment, infrastructure, settlements, and services. The results obtained allowed for a redefinition of proportional relationships among the various areas of the city of Pescara, based on a specific Urban Quality Index, and a recalculation of the real estate market. The study reveals different levels of desirability in the real estate market depending on the urban quality of the considered micro-zone, defined according to physical parameters linked to the four macro-systems.

Barreca, Curto, and Rolando [17] delved into the relationship between urban vibrancy and housing prices by developing an index called the Neighborhood Services Index (NeSI), determined by considering a set of variables related to neighborhood services. The influence of urban vibrancy on the listing prices of existing properties is analyzed by identifying different spatial clusters and applying spatial autoregressive models. The results confirm that housing prices and NeSI are statistically associated. However, they also highlight that in the most vulnerable areas of the city, urban vibrancy does not significantly influence the real estate market, as other social and housing factors (such as low educational levels or the presence of homeless individuals) have a stronger and negative impact on prices.

Numerous academic studies have confirmed that the territorial segmentation of the real estate market reflects the heterogeneity of the physical characteristics of the urban area in question, such as the presence of services like schools, green spaces, social centers, public spaces, or police departments [18,19,20].

Regarding the interaction between purely socio-economic factors (e.g., wealth and employment levels) and the real estate market, this relationship was explored by Geipele et al. [21] with reference to the Latvian market during the period from 2009 to 2013. The results of the research highlight the importance of the connection between quality of life and the real estate sector both in the short and long term. The correlation index between Latvian GDP and the average price per square meter during the period from 2004 to 2009 is high (0.9482), demonstrating a significant impact of GDP dynamics on real estate prices.

The relevance of social, cultural, and economic factors to real estate investment decisions, which subsequently influence market dynamics, was highlighted by Nguyen et al. [22,23]. Using various qualitative and quantitative models, the authors identified the influence of these variables on the real estate market.

Kueh and Chiew [24] analyzed factors influencing house buyers’ purchasing decisions and found that these decisions are shaped by several factors, including financial situation, home location, area security, and public amenities.

Lieser and Groh [25], in their research, developed a model to determine the relationship between the factors affecting real estate investment. The assumed independent variables include, among others, economic activity (economic scale, GDP per capita, GDP growth, labor force, inflation, and changes due to new technology), investment opportunities (the degree of urbanization, the urban population, the quality of infrastructure, and the development of the service sector), and the socio-cultural and political environment (human development and crime rate). The results demonstrate the influence of these variables on real estate investment decisions.

Examining the relationship from a different point of view—thus starting from the impact of real estate values on life quality—this perspective has been studied in the literature over the past two decades in various contexts.

A study by Castriota [26] explored how fluctuations in house prices and stock prices have a significant effect on household wealth and, consequently, on consumption models. The author highlights that the existing literature often reveals a non-linear relationship between various determinants of human well-being, such as consumption capacity and happiness. In other words, a higher income does not automatically lead to greater well-being, as other factors also play a significant role. In his research, the author analyzed the effects of real estate and stock market fluctuations on the self-reported personal satisfaction levels of about 400,000 Western European citizens from 1975 to 2002. The results show that increases in house and stock prices positively affect happiness, with real estate prices having a greater impact than stock market prices.

By dividing citizens into clusters (e.g., by income, age, etc.), the analysis revealed that low-income individuals are the most sensitive to increases in real estate and stock prices.

Consistent with the findings of Castriota, numerous contributions from the existing literature across different geographical contexts confirm the positive effect that the real estate market has historically had on consumption models. These include studies by Skinner [27] for the United States, Yashikawa and Ohtake [28] for Japan, Case [29] for New England, Brodin and Nymoen [30] for Norway, Koskela et al. [31] for Finland, Bayoumi [32] for the United Kingdom, Engelhardt [33] for Canada, and Berg and Bergström [34] for Sweden.

Rogerson, in his research [6], considered real estate prices and housing accessibility as factors contributing to determining the quality of life in cities, referring to the extensive literature to support this argument [35,36,37,38]. In his work, the level of life quality in cities is attributed to different variables, including environmental factors (pollution and air quality), social factors (crime, education, and culture), economic factors (employment), urban factors (infrastructure and transport), as well as real estate factors (property prices and housing accessibility). The aim of the study is to highlight how the overall quality of life contributes to determining the competitiveness of urban contexts at both national and international scales.

Including life quality in the determination of a competitive context thus helps define the attractiveness of that context, even for investors external to it [39]. This situation naturally generates financial benefits from such investments, in the form of higher wages, new jobs, and higher housing prices [40,41].

Currently, there are multiple national and international rankings of cities with the best life quality. Despite the significant discretion involved in choosing the indicators, the influence these rankings exert, generally, on public opinion and specifically on the decision-making processes of retail and professional investors is undeniable. This factor inevitably impacts market dynamics, including the real estate sector.

4. Case Study

4.1. Methodology

The objective of this study is to empirically assess the influence of life quality on real estate market dynamics in a specific context. In light of this goal, the adopted methodology is based on a comparative statistical analysis between the key parameters of the residential real estate market and a benchmark representing the quality of life in the specific territorial context. Furthermore, by breaking down the QoL index into its various indicators, six indicators are selected for analysis, each corresponding to a different macro-category, as explained in further detail below. These indicators are then compared with real estate market variables. The analysis is conducted on a sample of 107 Italian municipalities and, for each municipality, local real estate market variables are compared with the selected external parameters of the specific context.

In light of the above, regarding the parameters adopted in the developed analysis, two are related to the real estate market—the OMI real estate quotations and the IMI indicator, both produced by the Real Estate Market Observatory of the Italian Revenue Agency [14]. Specifically, the quotations provided by the Real Estate Market Observatory of the Italian Revenue Agency are published semi-annually and identify a minimum/maximum range of market and rental values per unit of surface area. These quotations refer to standard real estate units classified within a specific building type and located in a homogeneous territorial area, known as the OMI zone. The minimum and maximum values represent standard market conditions; therefore, properties with extraordinary features for a given building type in their respective zone are not included in the range. The semi-annual survey, conducted through direct investigation, covers municipalities recorded in the cadastral archives that exhibit sufficient market activity (over 1400 municipalities, which account for approximately 65% of the national real estate stock and about 70% of national transactions). For each building type, a sufficiently large sample is selected to establish a meaningful value range (confidence interval) for the homogeneous zone under analysis. The primary sources of the survey include regularly registered sales deeds and lease contracts. In the selected dataset, each quotation is calculated as the average of the median values of the intervals related to the residential types of the OMI zones of each municipality. Specifically, the dataset is composed of the annual average value for 107 Italian municipalities in the year 2023.

The trend in real estate transactions affects the values of the IMI (real estate market intensity), a parameter introduced by the Italian Revenue Agency as an indicator of the real estate market’s dynamism. This indicator is calculated as the ratio between the number of transactions completed in a given year, expressed in terms of normalized transactions (NTNs), which accounts for the number of transactions based on the ownership share being transferred, and the available market stock, i.e., the total number of existing real estate units in a specific geographic area as recorded in the Land Registry. The higher the IMI indicator, the more dynamic the real estate market is. Conversely, a low IMI value reflects sluggish transaction activity and a higher number of unsold properties at the end of the year.

As for the remaining parameters, they are derived from the QoL ranking (2024) published by Il Sole 24 Ore [15]. This ranking evaluates Italian provinces based on where people enjoy the highest quality of life using 90 indicators divided into six thematic macro-categories (each consisting of 15 indicators): (i) wealth and consumption; (ii) business and employment; (iii) environment and services; (iv) demographics, society, and health; (v) justice and security; (vi) culture and leisure.

The indicators are all sourced from official sources, institutions, and research institutes (such as the Ministry of the Interior [42], the Ministry of Justice [43], the National Statistical Institute—ISTAT [44], etc.) or provided by certified entities (including Scenari Immobiliari [45], Prometeia [46], and Infocamere [47]). For each of the 90 indicators, the province with the best value receives 1000 points, while the one with the worst value receives zero points, based on a parameter’s defined “reading direction” (positive or negative) as established by the editorial team. The scores for the other provinces are distributed according to their distance from these extremes (1000 and 0). Subsequently, for each of the six macro-categories, a ranking is determined based on the average score obtained across the 15 indicators, each equally weighted (1/90). Finally, the overall ranking is constructed using the simple arithmetic means of the six sector rankings.

In addition to the total score of the QoL index assigned to each province and included in the dataset, this analysis also considers some of the indicators used in compiling the ranking. Specifically, six indicators were selected, each corresponding to a different macro-category: (i) household expenditure on the consumption of durable goods; (ii) employment rate; (iii) crime indicator; (iv) total migration balance; (v) urban ecosystem; (vi) the accessibility of essential services indicator.

The indicator related to household expenditure on the consumption of durable goods is expressed in euros per year and is based on the 2022 analysis conducted by the Findomestic—Prometeia Observatory [46].

The employment rate is expressed as a percentage for the 20–64 age group and is derived from the 2022 ISTAT survey [44].

The crime indicator—total reported crimes—is expressed as the number of reports per 100,000 inhabitants and is based on the 2022 ISTAT survey and data from the Department of Public Security of the Ministry of Interior [48].

The total migration balance, sourced from the 2022 ISTAT survey [44], represents the difference between the number of individuals registered and those removed from the population registers due to internal migration, international migration, or other reasons.

The urban ecosystem indicator is a composite parameter developed by Legambiente (2022) [49] based on 18 parameters related to air quality, waste management, water networks, and land consumption. Specifically, the considered indicators are nitrogen dioxide (NO2), fine dust (PM10), ozone (O3), domestic water consumption, leakage water network, wastewater treatment, municipal waste generation, municipal waste recycling, urban public transport passengers, urban public transport supply, car motorization rate, road accidents, urban cycle paths, safety islands, urban green areas, trees in urban areas, renewable energy, and efficient land use.

The accessibility of essential services indicator is expressed as the average road travel time to reach the nearest hub (in minutes) and is based on an ISTAT analysis for the year 2021 [44].

In

Table 1. A summary of the indexes and indicators considered in the analysis.

Variable	Acronym	Source (Accessed on 25 March 2025)
Life Quality index	QoL	https://lab24.ilsole24ore.com
Real Estate Market Intensity	IMI	https://www.agenziaentrate.gov.it
OMI real estate quotations	QUO	https://www.agenziaentrate.gov.it/portale/schede/fabbricatiterreni/omi/banche-dati/quotazioni-immobiliari
Household expenditure on the consumption of durable goods	SPE	https://www.osservatoriofindomestic.it/
Employment rate	OCC	https://www.istat.it/
Crime indicator	CRI	https://www.interno.gov.it/it/ministero/dipartimenti/dipartimento-pubblica-sicurezza + https://www.istat.it/
Total migration balance	MIG	https://www.istat.it/
Urban ecosystem indicator	ECO	https://www.legambiente.it/
Accessibility of essential services indicator	ACC	https://www.istat.it/

, a summary of the indexes and indicators considered in the analysis is reported.

4.2. Results

For each comparison between the selected indicators/indexes and the real estate variables, results are shown in a graphical overview and, for higher clarity, the input dataset has been divided into five clusters according to their articulation on Italian territory (Northeast Italy, Northwest Italy, Central Italy, Southern Italy, and the Islands). The main outcomes for each comparison are commented on below. The elaborated graphs related to the QoL index and the real estate factors are reported below, while all of the remaining ones are illustrated in a Supplementary File (Figures S1–S5).

The analysis has been articulated in two steps—in the first, the data have been described in order to capture, with the support of bar charts, the given behavior and any relationships. The second phase involved the normalization and georeferencing of the values assumed by the variables to display the data on a map, to perform a correlation analysis, and to identify any spatial relationships and spatial autocorrelations.

Since some variables can assume negative values, the normalization was performed by assigning “0” to the minimum value and “1” to the maximum value according to the following formula

$$y={\displaystyle \frac{x-MIN}{MAX-MIN}}$$

where x represents the value assumed by the variable, MIN (or MAX) the minimum (or maximum) value included in the starting database, and y the normalized value on the 0–1 scale.

From the comparison between the QoL index, 2023 real estate prices and 2023 IMI, the analysis of the five identified clusters (Northeast Italy, Northwest Italy, Central Italy, Southern Italy, and the Islands) shows that the municipalities with the highest property values and QoL index scores are the cities of Milan and Bolzano. In fact, the local real estate segments of these cities record the highest values on a national scale, specifically the city of Bolzano at 4049 EUR/m² and the city of Milan at 4716 EUR/m². This is consistent with their QoL scores (Bolzano: 635.09 and Milan: 619.47), compared to the highest national value of 640.52 recorded in the city of Bergamo. The results are also consistent with the IMI values, where the city of Milan ranks among the highest (3). Similarly, the cities with the lowest QoL index scores also show some of the lowest real estate prices. On a national scale, the lowest QoL values are found in the cities of Crotone (447.26) and Reggio Calabria (436.10). Consistently with this, local property prices are among the lowest in the sample, with the city of Crotone at 938 EUR/m² and the city of Reggio Calabria at 796 EUR/m². The same trend is observed in the IMI 2023 ranking, where the city of Reggio Calabria holds the lowest position (1.10). An exception is represented by the city of Naples, which, despite having a low QoL score (443.23), ranking second-to-last in the overall classification, still shows property prices above the national average (2149 EUR/m²) and an IMI value slightly below the general average (1.81 compared to an average of 2.23).

In Figure 1 the histograms related to the QoL index and the real estate variables—OMI quotations (QUO) and IMI indicator—for the five identified clusters are reported.

With reference to the crime indicator—total reported offenses—the highest national value for this parameter is recorded in the city of Milan. However, this does not appear to affect real estate dynamics or market values. A similar trend is observed in the cities of Rome and Florence, which, despite having high crime indicator values (Rome: 6071.30 and Florence: 6053.80), are among the cities with the highest real estate prices in Italy (Rome: 2760 EUR/m² and Florence: 2813 EUR/m², compared to a national average of around EUR 1500). The reason for this could be that these cities are among the country’s main cultural, tourist, and business hubs. As a result, real estate market mechanisms seem to overlook the high number of reported crimes, as these are offset by other factors (cultural, tourism-related, and employment opportunities). The lowest crime indicator values are recorded in the cities of Oristano and Potenza (1510.80 and 1934.50, respectively), where however, property prices are only slightly below the national average (around 1260 EUR/m²).

With regard to the employment rate, there is a certain consistency between the highest values recorded nationwide and real estate prices. The city of Bolzano has the highest employment rate (79.60), ranking second in terms of average property value and having an IMI value slightly above the national average (2.48 compared to the average of 2.23). The lowest employment rates are recorded in the municipalities of Caltanissetta and Reggio Calabria (41.20 and 45, respectively), which also have the lowest real estate prices in the sample (Caltanissetta: 568 EUR/m² and Reggio Calabria: 796 EUR/m²). Additionally, three northern Italian municipalities—Cuneo, Novara, and Vercelli—show some divergence from this trend. Despite having some of the highest employment rates (Cuneo: 75.10; Novara: 74.80; Vercelli: 71.40), their real estate values remain below the national average (Cuneo: 1329 EUR/m²; Novara: 1004 EUR/m²; Vercelli: 899 EUR/m²). This could be due to the fact that, while these cities have active industrial hubs that generate a strong job market, this alone is not enough to make them attractive from a real estate perspective.

Regarding the urban ecosystem indicator, which considers various factors related to urban green spaces, air quality, city infrastructure, and transport systems, as well as waste production and collection, the highest value is recorded in the city of Reggio Emilia (80.70). However, property prices in this municipality remain below the national average (1351 EUR/m²), although the IMI still indicates a certain level of dynamism in the real estate market (2.82). Similarly, in the cities of Pordenone and Forlì, high urban ecosystem indicator values are observed alongside relatively low real estate prices. As for the cities ranking lowest in the urban ecosystem indicator, the bottom positions are occupied by the cities of Catania, Reggio Calabria, and Crotone, with respective scores of 15.80, 26.40, and 30.60. Consistently with this, real estate prices in these cities are among the lowest, averaging around 950 EUR/m².

For the indicator related to the household expenditure on the consumption of durable goods, among the municipalities with high values the cities of Bolzano (3567 EUR/year) and Florence (3470 EUR/year) show consistency with average high real estate prices (Bolzano 4049 EUR/m² and Florence 2813 EUR/m²). With reference to the city of Naples, although a value of household expenditure lower than the recorded average one is attested (2083 EUR/year compared to the average value equal to 2800 EUR/year), a substantial alignment with real estate prices is noted (2149 EUR/m²). Even for the Italian municipalities for which the lowest values of household expenditure on the consumption of durable goods are observed, a correspondence is highlighted in terms of reduced real estate values. In particular, for the city of Crotone, household expenditure is equal to 1687 EUR/year and average real estate prices of 938 EUR/m² are detected, whereas for the city of Agrigento, the household expenditure is 1858 EUR/year and an average value of 828 EUR/m² is found. In this sense, it is clear that the spending capacity of families living in specific geographical contexts influences the accessibility of purchasing properties and this is reflected in local property values.

From the comparison between the values recorded for the total migration balance and property prices, among the municipalities for which the highest values are detected, referring to the indicator, are the cities of Pavia (12.5), Prato (10.5), and Savona (10.5). For these cities, levels of real estate prices slightly higher than the national average are noted (Pavia 1841 EUR/m², Prato 1741 EUR/m², and Savona 2075 EUR/m² compared to the national average value of 1500 EUR/m²). Also, for the IMI, values in line with the national average are found (Pavia 2.42, Prato 2.46, and Savona 2.14, compared to the national average value of 2.23).

The lowest value of the total migration balance is reported for the city of Caltanissetta (−5.3), where the lowest value of real estate prices of the entire ranking is also identified (578 EUR/m²). The obtained results confirm that the resident population’s attitude to relocation is reflected in local real estate values as it directly influences market demand.

The lowest values for the indicator of accessibility to essential services are found for the cities of Gorizia (14.20 min) and Venice (13.30 min), both located in the Italian region of Veneto, and point to a lower average travel time to reach the first essential pole. However, for the city of Gorizia, the real estate market does not absorb this circumstance, presenting an average real estate price of 919 EUR/m², whereas for the city of Venice the indicator is reflected in the moderate level of local real estate values (2387 EUR/m²).

The highest value for the accessibility indicator is found for the city of Nuoro (72.90 min), for which the level of prices, although lower than the national average, is not at the minimum (1263 EUR/m²). Similarly, for the city of Enna, the accessibility indicator is quite high (51.30 min) in the face of low real estate values (987 EUR/m²). It should be noted that both cities in which it is more difficult to reach an essential service are located on Italian islands, and this condition could make reaching these poles more difficult.

Starting from the variables that flow into the QoL, the correlation analysis carried out on the normalized values points to a high positive correlation between the SPE, OCC, MIG, and QoL variables (between a minimum of 0.73 and a maximum of 0.92). The ECO variable has a moderate positive correlation with the SPE, OCC, MIG, and QoL variables (between 0.53 and 0.69), whereas the CRI and ACC variables are poorly correlated. These results could indicate that the Italian provinces in which a high employment rate (OCC) is attested are attractive for workers and, therefore, have a positive migration balance (MIG). Consequently, families have a greater spending capacity for the consumption of durable goods (SPE)—these economic variables play an important role in achieving a high quality of life (QoL).

For the environmental (ECO) and social (CRI and ACC) variables, it is not possible to detect significant correlations due to the multiple aspects they encompass. Similarly, with reference to the variables representing the real estate market, a moderate positive correlation with each other is observed, probably because when a high number of transactions (IMI) is found, the market is more dynamic and, thus, higher prices are recorded (OMI), and vice versa.

Table 2. Correlation analysis between the variables considered in the analysis.

	SPE	OCC	CRI	MIG	ECO	ACC	QoL	QUO	IMI		I-Moran
SPE	1.00	0.92	0.29	0.79	0.57	−0.36	0.85	0.43	0.64		0.84
OCC		1.00	0.26	0.84	0.64	−0.35	0.91	0.47	0.59		0.71
CRI			1.00	0.36	−0.01	−0.21	0.17	0.61	0.35		0.15
MIG				1.00	0.53	−0.39	0.73	0.34	0.59		0.76
ECO					1.00	−0.13	0.69	0.25	0.42		0.31
ACC						1.00	−0.42	−0.16	−0.41		0.34
QoL							1.00	0.47	0.64		0.71
QUO								1.00	0.41		0.24
IMI									1.00		0.71
	−1.00	0.00	1.00

shows the correlation analysis carried out between the variables considered in the analysis.

With regard to the analysis of the geographical distribution of the normalized values assumed by the variables and the spatial autocorrelation returned by the I-Moran index, it should be pointed out that the positively correlated ones (SPE, OCC, MIG, and QoL) have a high spatial autocorrelation. Furthermore, higher values are detected in the provinces of Northern Italy, intermediate values in those of Central Italy, and lower values in the Islands and Southern Italy. For the variables CRI, ECO, and ACC, what was previously noted is valid, whereas for the real estate market, a behavior that is more difficult to interpret is revealed—the prices (QUO) have a more inhomogeneous geographical distribution from a geographical point of view unlike the number of transactions (IMI).

In Figure 2, a spatial distribution of the results obtained for each indicator and index analyzed in the present research is shown. It should be recalled that the values attributed to each provincial capital reflect data that are specific solely to the main city’s territory. Furthermore, it should be noted that the map, for visualization purposes, was created by extending the highlighted areas to cover the entire provincial territory, although the considered data exclusively refer to the municipal territory of the provincial capitals.

5. Conclusions

The relationship between humans, the natural environment, and the anthropized space is, by its nature, complex, and constitutes a trinomial whose components are in a constant dynamic relationship. For this reason, numerous social sciences studies concern the topic of individual and collective well-being not only in terms of economic wealth but also of life quality. This aspect is influenced by a wide number of specific characteristics of the urban context (social, economic, environmental, urban planning, landscape, cultural nature, etc.). Simultaneously, the real estate market reflects and values the unique characteristics associated with a property’s location within the urban environment, considering the infrastructural, environmental, and architectural aspects [50]. In this regard, property values can be seen as an indirect indicator of the ability of a physical space to enhance both individual and collective well-being for those who inhabit it [51].

Focusing on the Italian context, this research has aimed to empirically assess how real estate market dynamics are influenced by the quality of life in a given area. Specifically, the study has sought to determine whether, and to what extent, real estate trends reflect the external characteristics of the urban environment and how real estate prices and market activity correspond to the observed life quality levels. To achieve this, the study has compared key indicators of the residential market segment—OMI prices and the IMI indicator (used as a proxy for market dynamism)—with the QoL index developed by Il Sole 24 Ore for selected provincial capitals, analyzing the consistency between these variables.

Furthermore, by deconstructing the QoL index into its various components, the analysis has pointed out the indicators most strongly associated with real estate market dynamics. This method has enabled an exploration of the relationships within each local context, revealing national-level differences and emphasizing the degree of geographical variation. More broadly, certain indicators, such as the employment rate and household expenditure on durable goods, showed alignment with real estate market trends across different local contexts. Using GIS-based analysis, the indicators were graphically represented to uncover any spatial correlations in phenomena where geographic factors play a crucial role, highlighting potential connections between the examined indicators, the geographical distribution of the normalized values of the variables, and spatial autocorrelation.

The contribution of this study lies in identifying the key factors most closely associated with the processes that influence the formation of property prices and market phenomena. These factors, in turn, significantly shape how potential investors perceive various Italian cities.

Future research could involve the application of econometric techniques by treating the identified indicators as independent variables and the real estate benchmarks as the dependent ones. This would allow for an empirical quantification of each factor’s influence on the real estate market. The findings could inform local policy strategies aimed not only at improving the quality of life but also at fostering real estate sector development.

Moreover, further insights of the research may concern a critical analysis of the Sole 24 Ore QoL index, compared to four main worldwide reports (e.g., the Global Liveability index, the Global power city index, the Innovation city index, and the Smart city index) in order to understand how the reference measurement system aligns with methods that are used internationally.

Note: The current study has been developed within the current research P.R.I.N. Project 2022: “INSPIRE—Improving Nature-Smart Policies through Innovative Resilient Evaluations”, Grant number: 2022J7RWNF.